Recent technological and methodological advances for the investigation of landslide dams

River-damming by landslides is a widespread phenomenon around the world. Recent advances in remote sensing technology and the rising commercial availability of their products enable the assemblage of increasingly more complete inventories and improve monitoring efforts. On the ground, multi-method dating campaigns enhance our understanding of the timelines of dam formation and failure. In comparison to single-dating methods, they reduce uncertainty by using different materials from the landslide deposit, facilitate the advantages of each method, and consider the deposit and the source area. They can pin dates on the time of lake drainage where backwater sediments are included in the dating campaign and thus inform about dam longevity. Geophysical methods provide non-invasive and rapid methods to investigate the properties and interior conditions of landslide dams. By identifying, e.g. evolving zones of weakness and saturation they can aid in the monitoring of a dam in addition to providing information on interior stratification for scientific research. To verify results from geophysical campaigns, and to add details of dam interior structures and geotechnical properties, knowledge of their sedimentology is essential. This information is gathered at sections from breached dams, other (partially) eroded landslide deposits, and through laboratory testing of sampled material. Combining the knowledge gained from all these methods with insights from blast-fill and embankment dam construction, physical and numerical modelling in multi-disciplinary research projects is the way forward in landslide dam research, assessment and monitoring. This review offers a broad, yet concise overview of the state-of-the-art in the aforementioned research fields. It completes the review of Fan et al. (2020) on the formation and impact on landslide dams

DFPENet-geology: A Deep Learning Framework for High Precision Recognition and Segmentation of Co-seismic Landslides

The following lists two main reasons for withdrawal for the public. 1. There are some problems in the method and results, and there is a lot of room for improvement. In terms of method, "Pre-trained Datasets (PD)" represents selecting a small amount from the online test set, which easily causes the model to overfit the online test set and could not obtain robust performance. More importantly, the proposed DFPENet has a high redundancy by combining the Attention Gate Mechanism and Gate Convolution Networks, and we need to revisit the section of geological feature fusion, in terms of results, we need to further improve and refine. 2. arXiv is an open-access repository of electronic preprints without peer reviews. However, for our own research, we need experts to provide comments on my work whether negative or positive. I then would use their comments to significantly improve this manuscript. Therefore, we finally decided to withdraw this manuscript in arXiv, and we will update to arXiv with the final accepted manuscript to facilitate more researchers to use our proposed comprehensive and general scheme to recognize and segment seismic landslides more efficiently.Comment: 1. There are some problems in the method and results, and there is a lot of room for improvement. Overall, the proposed DFPENet has a high redundancy by combining the Attention Gate Mechanism and Gate Convolution Networks, and we need to further improve and refine the results. 2. For our own research, we need experts to provide comments on my work whether negative or positiv

Modelling the role of material depletion, grain coarsening and revegetation in debris flow occurrences after the 2008 Wenchuan earthquake

A large amount of debris was generated by the co-seismic mass wasting associated with the 2008 Mw 7.9 Wenchuan earthquake. The abundance of this loose material along the slopes caused more frequent debris flows, triggered by less intense and/or shorter rainfalls. However, both the triggering rainfall and the debris flow frequency seem to have normalised progressively during the past decade. Although changes of rainfall thresholds for post-seismic debris flows were recorded after several major earthquakes, the factors controlling these changes remain poorly constrained. With the aid of a virtual experiment, we investigate the roles of material depletion, grain coarsening and revegetation of the co-seismic debris on the propagation and deposition of debris flows initiated by runoff, as well as their influence on the triggering rainfall thresholds. We employ a Geographic Information System (GIS)-based simulation of debris flow initiation by runoff erosion, which we first calibrate on the 14th August 2010 Hongchun gully event that occurred near the Wenchuan earthquake epicentre. We obtain, by investigating each of the aforementioned processes, changing critical rainfall intensity-duration thresholds for given debris flow runout distances. Grain coarsening appears to play a major role, which is consistent with published laboratory experiments, while material depletion and revegetation do not seem able to account alone for the actual quick decay of debris flow frequency. While the virtual experiment has proven useful in identifying the first-order controls on this decay, model improvements and verification over multiple catchments are needed to make the results useful in hazard assessments

Identifying post-earthquake debris flow hazard using Massflow

Catastrophic debris flows are common after large earthquakes and pose a significant risk for recovering communities. The depositional volume of these large debris flows is often much greater than the initiation volume, suggesting that bulking of the flow plays an important role in determining their volume, speed, and runout distance. Observations from recent earthquakes have driven progress in understanding the relationship between triggering rainfall events and the timing of post-earthquake debris flows. However, we lack an adequate mechanism for quantifying bulking and applying it within a hazard context. Here we apply a 2D dynamic debris flow model (Massflow) that incorporates a process-based expression of basal entrainment to understand how debris flow bulking may occur within post-earthquake catchments and develop hazard maps. Focussing on catchments in the epicentral area of the 2008 Mw 7.9 Wenchuan Earthquake, we first parameterised the model based on a large debris flow that occurred within the Hongchun catchment, before applying the calibrated model to adjoining catchments. A model sensitivity analysis identified three main controls on debris flow bulking; the saturation level of entrainable material along the flow pathway, and the size and position of initial mass failures. The model demonstrates that the difference between small and very large debris flows occur across a narrow range of pore-water ratios (λ). Below λ = 0.65 flows falter at the base of hillslopes and come to rest in the valley bottom, above λ = 0.70 they build sufficient mass and momentum to sustain channelised flow and transport large volumes of material beyond the valley confines. Finally, we applied the model across different catchments to develop hazard maps that demonstrate the utility of Massflow in post-earthquake planning within the Wenchuan epicentral region

The landslide story

The catastrophic Wenchuan earthquake induced an unprecedented number of geohazards. The risk of heightened landslide frequency after a quake, with potential secondary effects such as river damming and subsequent floods, needs more focused attention

Automated Mapping of Ms 7.0 Jiuzhaigou Earthquake (China) Post-Disaster Landslides Based on High-Resolution UAV Imagery

The Ms 7.0 Jiuzhaigou earthquake that occurred on 8 August 2017 triggered hundreds of landslides in the Jiuzhaigou valley scenic and historic-interest area in Sichuan, China, causing heavy casualties and serious property losses. Quick and accurate mapping of post-disaster landslide distribution is of paramount importance for earthquake emergency rescue and the analysis of post-seismic landslides distribution characteristics. The automatic identification of landslides is mostly based on medium- and low-resolution satellite-borne optical remote-sensing imageries, and the high-accuracy interpretation of earthquake-triggered landslides still relies on time-consuming manual interpretation. This paper describes a methodology based on the use of 1 m high-resolution unmanned aerial vehicle (UAV) imagery acquired after the earthquake, and proposes a support vector machine (SVM) classification method combining the roads and villages mask from pre-seismic remote sensing imagery to accurately and automatically map the landslide inventory. Compared with the results of manual visual interpretation, the automatic recognition accuracy could reach 99.89%, and the Kappa coefficient was higher than 0.9, suggesting that the proposed method and 1 m high-resolution UAV imagery greatly improved the mapping accuracy of the landslide area. We also analyzed the spatial-distribution characteristics of earthquake-triggered landslides with the influenced factors of altitude, slope gradient, slope aspect, and the nearest faults, which provided important support for the further study of post-disaster landslide distribution characteristics, susceptibility prediction, and risk assessment.This work was funded by the National Key Research and Development Program of China (Project No. 2018YFC1505202), the National Natural Science Foundation of China (41941019), the State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Independent Research Project (SKLGP2020Z012), the project on identification and monitoring of potential geological hazards with remote sensing in Sichuan Province (510201202076888) and the Everest Scientific Project at Chengdu University of Technology (2020ZF114103)

Post-disaster assessment of 2017 catastrophic Xinmo landslide (China) by spaceborne SAR interferometry

Timely and effective post-disaster assessment is of significance for the design of rescue plan, taking disaster mitigation measures and disaster analysis. Field investigation and remote sensing methods are the common ways to perform post-disaster assessment, which are usually limited by dense cloud coverage, potential risk, and tough transportation etc. in the mountainous area. In this paper, we employ the 2017 catastrophic Xinmo landslide (Sichuan, China) to demonstrate the feasibility of using spaceborne synthetic aperture radar (SAR) data to perform timely and effective post-disaster assessment. With C-band Sentinel-1 data, we propose to combine interferometric coherence to recognize the stable area, which helps us successfully identify landslide source area and boundaries in a space-based remote sensing way. Complementarily, X-band TanDEM-X SAR data allow us to generate a precise pre-failure high-resolution digital elevation model (DEM), which provides us the ability to accurately estimate the depletion volume and accumulation volume of Xinmo landslide. The results prove that spaceborne SAR can provide a quick, valuable, and unique assistance for post-disaster assessment of landslides from a space remote sensing way. At some conditions (bad weather, clouds, etc.), it can provide reliable alternative.This work was funded by Sichuan Science and Technology Plan Key Research and Development Program (Grant No. 2018SZ0339), National Natural Science Foundation of China (Grant No. 41801391), State Key Laboratory of Geodesy and Earth’s Dynamics Open fund (Grant No. SKLGED2018-5-3-E), The Funds for Creative Research Groups of China (Grant No. 41521002) and partially supported by the Spanish Ministry of Economy, Industry and Competitiveness (MINECO), the State Agency of Research (AEI), and European Funds for Regional Development (FEDER), under project TIN2014-55413-C2-2-P and by the Spanish Ministry of Education, Culture and Sport, under project PRX17/00439. This work was also supported by the National Environment Research Council (NERC) through the Centre for the Observation and Modeling of Earthquakes, Volcanoes and Tectonics (COMET, ref.: come30001), the LiCS project (ref. NE/K010794/1), the ESA-MOST DRAGON-4 project (ref. 32244), and the Hunan Province Key Laboratory of Coal Resources Clean-Utilization and Mine Environment Protection, Hunan University of Science and Technology (Ref. E21608)

The fate of sediment after a large earthquake

Large earthquakes rapidly denude hillslopes by triggering thousands of coseismic landslides. The sediment produced by these landslides is initially quickly mobilised from the landscape by an interconnected cascade of processes. This cascade can dramatically but briefly enhance local erosion rates. Hillslope and channel processes, such as landsliding and debris flows, interact to influence the total mass, caliber, and rate of sediment transport through catchments. Calculating the sediment budget of an earthquake lends insight into the nature of these interactions. Using satellite imagery derived landslide inventories, channel surveys and a literature review combined with a Monte Carlo simulation approach we present a constrained sediment budget of the first decade after the 2008 Mw7.9 Wenchuan earthquake. With this sediment budget we demonstrate that debris flows are dominant process for delivering sediment into channels and that large volumes of sediment remain in the landscape. In our study area over 88% (469.7 Mega tonnes) of the coseismically generated sediment remains on the hillslopes in 2018. Of the 12% of the sediment that was mobilised, 67% (45.2 ± 22 Mt) was mobilised by debris flows. Despite the large proportion of sediment remaining on the hillslope, the frequency of debris flows declined significantly over our observation period. The reduction in debris-flow frequency is not correlated to reductions in the frequency of triggering storms, suggesting changes in the mechanical properties of hillslope sediment may drive this observation. The stabilization of coseismically generated sediment greatly extends its residence time and may influence catchment sediment yields for centuries or millennia